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Description

Coordinated multi-vehicle autonomous systems have the potential to
provide incredible functionality, but off-nominal conditions and
degraded system components can render this capability ineffective.
This research effort is focused on developing techniques to improve
mission-level functional reliability through better system
self-awareness and adaptive mission planning. This extends the
traditional definition of health management, which has historically
referred to the process of actively monitoring and managing vehicle
sub-systems (e.g., avionics) in the event of component failures, to
the context of multiple vehicle operations and autonomous multi-agent
teams. In this case, health management information about each mission
system component is used to improve the mission system's
self-awareness and adapt vehicle, guidance, task and mission plans.
As discussed in the following, the focus of our work has been on
establishing the theoretical foundations of health-aware task
planning, developing tractable solution algorithms to the resulting
optimizations, and using a unique multi-UAV testbed called RAVEN to
experimentally demonstrate the benefits of the new approach.